Solutions: Security & Surveillance

Imagu breaks the current barriers in object recognition by addressing its two major challenges – accurate object recognition and classification and reduction of development risks and costs. Imagu’s technology is suited for both government and commercial surveillance, offering an automated, cost-effective and robust solution.

Current image analysis software are limited to detecting change or motion and not the objects themselves. In addition, developers of image analysis software need, to date, to develop application-specific software for each individual application. Inherent in this process are four major hurdles:

High misdetection rates and false alarms;

The development process is an extremely costly process to undertake;

It often takes several years to complete;

There is no guarantee of success at the end and reaching “proof of concept” may require a very long time.

Implementing a new approach, Imagu’s innovative software solution effectively overcomes these hurdles and raises object recognition to a new level. For more information on our technology and software solutions, please refer to the Technology section of our web site.

Key Features

Precise object location

Identification of object sub-elements. (e.g. human arms and legs)

Substantial reduction of development risks and costs and time to market: feasibility or “proof of concept” is reached within weeks; development of new applications, in accordance with customer requirements and goals, can be achieved within just a few months.

Enables recognition even when the object is not clearly separated from background

Recognition of still objects in single images since detection is not motion or change based

Support for all image sources including black & white, color, Infra-Red, and Radar.

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Detection of cars in infra-red airborne imagery. In this example the challenge was to detect cars and armored vehicles and to differentiate between their different types, while allowing for an extremely small rate of false detections and misdetection. Note that special attention is required to differentiate the object of interest from “noise” in the image (e.g. rocks) to minimize misdetection.